Delete streamlit_app.py

streamlit_app.py

@@ -1,141 +0,0 @@
-import os  # Add this import to use os.path.splitext
-import csv
-import streamlit as st
-import polars as pl
-from io import BytesIO, StringIO
-from gliner import GLiNER
-from gliner_file import run_ner
-import time
-
-st.set_page_config(page_title="GliNER", page_icon="🧊", layout="wide", initial_sidebar_state="expanded")
-
-# Modified function to load data from either an Excel or CSV file
-@st.cache_data
-def load_data(file):
-    _, file_ext = os.path.splitext(file.name)
-    if file_ext.lower() in ['.xls', '.xlsx']:
-        return pl.read_excel(file)
-    elif file_ext.lower() == '.csv':
-        file.seek(0)  # Retour au début du fichier
-        try:
-            sample = file.read(4096).decode('utf-8')  # Essayer de décoder l'échantillon en UTF-8
-            encoding = 'utf-8'
-        except UnicodeDecodeError:
-            encoding = 'latin1'  # Basculer sur 'latin1' si UTF-8 échoue
-            file.seek(0)
-            sample = file.read(4096).decode(encoding)
-        
-        file.seek(0)
-        dialect = csv.Sniffer().sniff(sample)  # Détecter le dialecte/délimiteur
-
-        # Convertir le fichier en StringIO pour simuler un fichier texte, si nécessaire
-        file.seek(0)
-        if encoding != 'utf-8':
-            file_content = file.read().decode(encoding)
-            file = StringIO(file_content)
-        else:
-            file_content = file.read().decode('utf-8')
-            file = StringIO(file_content)
-        
-        return pl.read_csv(file, separator=dialect.delimiter, truncate_ragged_lines=True, ignore_errors=True)
-    else:
-        raise ValueError("The uploaded file must be a CSV or Excel file.")
-
-
-# Function to perform NER and update the UI
-def perform_ner(filtered_df, selected_column, labels_list):
-    ner_results_dict = {label: [] for label in labels_list}
-    
-    progress_bar = st.progress(0)
-    progress_text = st.empty()
-    
-    start_time = time.time()  # Enregistrer le temps de début pour le temps d'exécution total
-
-    for index, row in enumerate(filtered_df.to_pandas().itertuples(), 1):
-        iteration_start_time = time.time()  # Temps de début pour cette itération
-        
-        if st.session_state.stop_processing:
-            progress_text.text("Process stopped by the user.")
-            break
-
-        text_to_analyze = getattr(row, selected_column)
-        ner_results = run_ner(st.session_state.gliner_model, text_to_analyze, labels_list)
-
-        for label in labels_list:
-            texts = ner_results.get(label, [])
-            concatenated_texts = ', '.join(texts)
-            ner_results_dict[label].append(concatenated_texts)
-
-        progress = index / filtered_df.height
-        progress_bar.progress(progress)
-        
-        iteration_time = time.time() - iteration_start_time  # Calculer le temps d'exécution pour cette itération
-        total_time = time.time() - start_time  # Calculer le temps total écoulé jusqu'à présent
-        
-        progress_text.text(f"Progress: {index}/{filtered_df.height} - {progress * 100:.0f}% (Iteration: {iteration_time:.2f}s, Total: {total_time:.2f}s)")
-
-    end_time = time.time()  # Enregistrer le temps de fin
-    total_execution_time = end_time - start_time  # Calculer le temps d'exécution total
-    
-    progress_text.text(f"Processing complete! Total execution time: {total_execution_time:.2f}s")
-    
-    for label, texts in ner_results_dict.items():
-        filtered_df = filtered_df.with_columns(pl.Series(name=label, values=texts))
-
-    return filtered_df
-
-def main():
-    st.title("Online NER with GliNER")
-    st.markdown("Prototype v0.1")
-
-    # Ensure the stop_processing flag is initialized
-    if 'stop_processing' not in st.session_state:
-        st.session_state.stop_processing = False
-
-    uploaded_file = st.sidebar.file_uploader("Choose a file")
-    if uploaded_file is None:
-        st.warning("Please upload a file.")
-        return
-
-    try:
-        df = load_data(uploaded_file)
-    except ValueError as e:
-        st.error(str(e))
-        return
-
-    selected_column = st.selectbox("Select the column for NER:", df.columns, index=0)
-    filter_text = st.text_input("Filter column by input text", "")
-    ner_labels = st.text_input("Enter all your different labels, separated by a comma", "")
-
-    filtered_df = df.filter(pl.col(selected_column).str.contains(f"(?i).*{filter_text}.*")) if filter_text else df
-    st.dataframe(filtered_df)
-
-    if st.button("Start NER"):
-        if not ner_labels:
-            st.warning("Please enter some labels for NER.")
-        else:
-            # Load GLiNER model if not already loaded
-            if 'gliner_model' not in st.session_state:
-                with st.spinner('Loading GLiNER model... Please wait.'):
-                    st.session_state.gliner_model = GLiNER.from_pretrained("urchade/gliner_largev2")
-                    st.session_state.gliner_model.eval()
-                    
-            labels_list = ner_labels.split(",")
-            updated_df = perform_ner(filtered_df, selected_column, labels_list)
-            st.dataframe(updated_df)
-
-            def to_excel(df):
-                output = BytesIO()
-                df.to_pandas().to_excel(output, index=False, engine='openpyxl')
-                return output.getvalue()
-
-            df_excel = to_excel(updated_df)
-            st.download_button(label="📥 Download Excel",
-                               data=df_excel,
-                               file_name="ner_results.xlsx",
-                               mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
-
-    st.button("Stop Processing", on_click=lambda: setattr(st.session_state, 'stop_processing', True))
-
-if __name__ == "__main__":
-    main()